Imagen de Portada

Anomaly Detection in Electric Vehicle Charging Stations Using Federated Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	C, Bishal K, Hilal, Amr, Thapa, Pawan
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Explainable Anomaly Detection for Electric Vehicles Charging Stations
por: Cederle, Matteo, et al.
Publicado: (2025)

Emerging Paradigms for Securing Federated Learning Systems
por: Abouelmagd, Amr Akmal, et al.
Publicado: (2025)

Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of Electric Vehicle Charging Stations
por: Zhang, Zongbao, et al.
Publicado: (2024)

An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework
por: Ghanim, Jihan, et al.
Publicado: (2024)

Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems
por: Marfo, William, et al.
Publicado: (2025)

Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case
por: Marfo, William, et al.
Publicado: (2025)

A Unified Variational Imputation Framework for Electric Vehicle Charging Data Using Retrieval-Augmented Language Model
por: Li, Jinhao, et al.
Publicado: (2026)

SynthCharge: An Electric Vehicle Routing Instance Generator with Feasibility Screening to Enable Learning-Based Optimization and Benchmarking
por: Daysalilar, Mertcan, et al.
Publicado: (2026)

Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques
por: Thapa, Ukesh, et al.
Publicado: (2024)

An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
por: Tajgardan, Mohsen, et al.
Publicado: (2026)

Online Electric Vehicle Charging Detection Based on Memory-based Transformer using Smart Meter Data
por: Kamoona, Ammar Mansoor, et al.
Publicado: (2024)

A Survey of Anomaly Detection in In-Vehicle Networks
por: Özdemir, Övgü, et al.
Publicado: (2024)

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
por: Dehrouyeh, Fatemeh, et al.
Publicado: (2024)

Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection
por: HaghighiFard, M. Saeid, et al.
Publicado: (2024)

Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series
por: Chen, Kuan-Cheng, et al.
Publicado: (2025)

STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection
por: Özer, Kadir-Kaan, et al.
Publicado: (2025)

Graph Evidential Learning for Anomaly Detection
por: Wei, Chunyu, et al.
Publicado: (2025)

Federated Learning for Early Prediction of EV Charging Demand
por: Perifanis, Vasilis, et al.
Publicado: (2026)

Enhancing Cross-Problem Vehicle Routing via Federated Learning
por: Meng, Xiangchi, et al.
Publicado: (2026)

Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
por: Belay, Mohammed Ayalew, et al.
Publicado: (2026)

Deep Learning for Time Series Anomaly Detection: A Survey
por: Darban, Zahra Zamanzadeh, et al.
Publicado: (2022)

Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication
por: Bastola, Ashish, et al.
Publicado: (2025)

Data-Driven Optimization of EV Charging Station Placement Using Causal Discovery
por: Junker, Julius Stephan, et al.
Publicado: (2025)

Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning
por: Gomaa, Amr, et al.
Publicado: (2024)

Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation
por: Dai, Wei, et al.
Publicado: (2024)

Modeling Electric Vehicle Car-Following Behavior: Classical vs Machine Learning Approach
por: Uddin, Md. Shihab, et al.
Publicado: (2025)

A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem
por: Daysalilar, Mertcan, et al.
Publicado: (2026)

A Scientific Machine Learning Approach for Predicting and Forecasting Battery Degradation in Electric Vehicles
por: Murgai, Sharv, et al.
Publicado: (2024)

Cloud-based Federated Learning Framework for MRI Segmentation
por: Prajapati, Rukesh, et al.
Publicado: (2024)

Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
por: Liu, Fangqi, et al.
Publicado: (2025)

EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection
por: Ren, Jing, et al.
Publicado: (2025)

FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
por: Anwar, Ahmed, et al.
Publicado: (2024)

Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
por: Kelleher, John D., et al.
Publicado: (2025)

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks
por: Chaudhary, Devashish, et al.
Publicado: (2026)

DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
por: Darban, Zahra Zamanzadeh, et al.
Publicado: (2024)

Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
por: Alihodžić, Adis, et al.
Publicado: (2026)

Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and Stochastic Programming
por: Puech, Alban, et al.
Publicado: (2024)

Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection
por: Lu, Ruiying, et al.
Publicado: (2025)

Learning Unified Representations of Normalcy for Time Series Anomaly Detection
por: Sarker, Prithul, et al.
Publicado: (2026)

Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis
por: Atzmueller, Martin, et al.
Publicado: (2024)